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Purpose: The image quality characteristics of two NEMA phantoms with yttrium-90 (90Y) were evaluated on a long axial field-of-view (AFOV) PET/CT. The purpose was to identify the optimized reconstruction setup for the imaging of patients with hepatocellular carcinoma after 90Y radioembolization. Methods: Two NEMA phantoms were used, where one had a 1:10 sphere to background activity concentration ratio and the second had cold background. Reconstruction parameters used are as follows: iterations 2 to 8, Gaussian filter 2- to 6-mm full-width-at-half-maximum, reconstruction matrices 440 × 440 and 220 × 220, high sensitivity (HS), and ultra-high sensitivity (UHS) modes. 50-, 40-, 30-, 20-, 10-, and 5-min acquisitions were reconstructed. The measurements included recovery coefficients (RC), signal-to-noise ratio (SNR), background variability, and lung error which measures the residual error in the corrections. Patient data were reconstructed with 20-, 10-, 5-, and 1-min time frames and evaluated in terms of SNR. Results: The RC for the hot phantom was 0.36, 0.45, 0.53, 0.63, 0.68, and 0.84 for the spheres with diameters of 10, 13, 17, 22, 28, and 37 mm, respectively, for UHS 2 iterations, a 220 × 220 matrix, and 50-min acquisition. The RC values did not differ with acquisition times down to 20 min. The SNR was the highest for 2 iterations, measured 11.7, 16.6, 17.6, 19.4, 21.9, and 27.7 while the background variability was the lowest (27.59, 27.08, 27.36, 26.44, 30.11, and 33.51%). The lung error was 18%. For the patient dataset, the SNR was 19%, 20%, 24%, and 31% higher for 2 iterations compared to 4 iterations for 20-, 10-, 5-, and 1-min time frames, respectively. Conclusions: This study evaluates the NEMA image quality of a long AFOV PET/CT scanner with 90Y. It provides high RC for the smallest sphere compared to other standard AFOV scanners at shorter scan times. The maximum patient SNR was for 2 iterations, 20 min, while 5 min delivers images with acceptable SNR. [ABSTRACT FROM AUTHOR]

Purpose: Image texture is increasingly used to discriminate tissues and lesions in PET/CT. For quantification or in computer-aided diagnosis, textural feature analysis must produce robust and comparable values. Because statistical feature values depend on image count statistics, we investigated in depth the stability of Haralick features values as functions of acquisition duration, and for common image resolutions and reconstructions. Methods: A homogeneous cylindrical phantom containing 9.6 kBq/ml Ge-68 was repeatedly imaged on a Siemens Biograph mCT, with acquisition durations ranging from three seconds to three hours. Images with 1.5, 2, and 4 mm isometrically spaced voxels were reconstructed with filtered back-projection (FBP), ordered subset expectation maximization (OSEM), and the Siemens TrueX algorithm. We analysed Haralick features derived from differently quantized (3 to 8-bit) grey level co-occurrence matrices (GLCMs) as functions of exposure E, which we defined as the product of activity concentration in a volume of interest (VOI) and acquisition duration. The VOI was a 50 mm wide cube at the centre of the phantom. Feature stability was defined for df/dE → 0. Results: The most stable feature values occurred in low resolution FBPs, whereas some feature values from 1.5 mm TrueX reconstructions ranged over two orders of magnitude. Within the same reconstructions, most feature value-exposure curves reached stable plateaus at similar exposures, regardless of GLCM quantization. With 8-bit GLCM, median time to stability was 16 s and 22 s for FBPs, 18 s and 125 s for OSEM, and 23 s, 45 s, and 76 s for PSF reconstructions, with longer durations for higher resolutions. Stable exposures coincided in OSEM and TrueX reconstructions with image noise distributions converging to a Gaussian. In FBP, the occurrence of stable values coincided the disappearance of negatives image values in the VOI. Conclusions: Haralick feature values depend strongly on exposure, but invariance exists within defined domains of exposure. Here, we present an easily replicable procedure to identify said stable exposure domains, where image noise does not substantially add to textural feature values. Only by imaging at predetermined feature-invariant exposure levels and by adjusting exposure to expected activity concentrations, can textural features have a quantitative use in PET/CT. The necessary exposure levels are attainable by modern PET/CT systems in clinical routine. [ABSTRACT FROM AUTHOR]

Purpose: A PET/CT system's imaging capabilities are best described by its point spread function (PSF) in the spatial domain or equivalently by its modulation transfer function (MTF) in the spatial frequency domain. Knowing PSFs or MTFs is a prerequisite for many numerical methods attempting to improve resolution and to reduce the partial volume effect. In PET/CT, the observed PSF is a convolution of the system's intrinsic imaging capabilities including image reconstruction (PSF0) and the positron range function (PRF) of the imaged β+ emitting isotope. A PRF describes the non-Gaussian distribution of β+ annihilation events around a hypothetical point source. The main aim was to introduce a new method for determining a PET/CT system's intrinsic MTF (MTF0) from phantom measurements of hot spheres independently of the β+ emitting isotope used for image acquisition. Secondary aim was to examine non-Gaussian and nonlinear MTFs of a modern iterative reconstruction algorithm. Methods: PET/CT images of seven phantom spheres with volumes ranging from 0.25 to 16 ml and filled either with 18F or with 68Ga were acquired and reconstructed using filtered back projection (FBP). MTFs were modeled with linear splines. The spline fit iteratively minimized the mean squared error between the acquired PET/CT image and a convolution of the thereof derived PSF with a numerical representation of the imaged hot phantom sphere. For determining MTF0, the numerical sphere representations were convolved with a PRF, simulating a fill with either 18F or 68Ga. The MTFs determined by this so-called MTF fit method were compared with MTFs derived from point source measurements and also compared with MTFs derived with a previously published PSF fit method. The MTF fit method was additionally applied to images reconstructed by a vendor iterative algorithm with PSF recovery (Siemens TrueX). Results: The MTF fit method was able to determine 18F and 68Ga dependent MTFs and MTF0 from FBP reconstructed images. Root-mean-square deviation between fit determined MTFs and point source determined MTFs ranged from 0.023 to 0.039. MTFs from Siemens TrueX reconstructions varied with size of the imaged sphere. Conclusions: MTF0 can be determined regardless of the imaged isotope, when using existing PRF models for the MTF fit method presented. The method proves that modern iterative PET/CT reconstruction algorithms have nonlinear imaging properties. This behaviour is not accessible by point source measurements. MTFs resulting from these clinically applied algorithms need to be estimated from objects of similar geometry to those intended for clinical imaging. [ABSTRACT FROM AUTHOR]